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  <h1>Source code for torchvision.models.resnet</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">load_state_dict_from_url</span>


<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;ResNet&#39;</span><span class="p">,</span> <span class="s1">&#39;resnet18&#39;</span><span class="p">,</span> <span class="s1">&#39;resnet34&#39;</span><span class="p">,</span> <span class="s1">&#39;resnet50&#39;</span><span class="p">,</span> <span class="s1">&#39;resnet101&#39;</span><span class="p">,</span>
           <span class="s1">&#39;resnet152&#39;</span><span class="p">,</span> <span class="s1">&#39;resnext50_32x4d&#39;</span><span class="p">,</span> <span class="s1">&#39;resnext101_32x8d&#39;</span><span class="p">,</span>
           <span class="s1">&#39;wide_resnet50_2&#39;</span><span class="p">,</span> <span class="s1">&#39;wide_resnet101_2&#39;</span><span class="p">]</span>


<span class="n">model_urls</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;resnet18&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/resnet18-5c106cde.pth&#39;</span><span class="p">,</span>
    <span class="s1">&#39;resnet34&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/resnet34-333f7ec4.pth&#39;</span><span class="p">,</span>
    <span class="s1">&#39;resnet50&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/resnet50-19c8e357.pth&#39;</span><span class="p">,</span>
    <span class="s1">&#39;resnet101&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/resnet101-5d3b4d8f.pth&#39;</span><span class="p">,</span>
    <span class="s1">&#39;resnet152&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/resnet152-b121ed2d.pth&#39;</span><span class="p">,</span>
    <span class="s1">&#39;resnext50_32x4d&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth&#39;</span><span class="p">,</span>
    <span class="s1">&#39;resnext101_32x8d&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth&#39;</span><span class="p">,</span>
    <span class="s1">&#39;wide_resnet50_2&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth&#39;</span><span class="p">,</span>
    <span class="s1">&#39;wide_resnet101_2&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth&#39;</span><span class="p">,</span>
<span class="p">}</span>


<span class="k">def</span> <span class="nf">conv3x3</span><span class="p">(</span><span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;3x3 convolution with padding&quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
                     <span class="n">padding</span><span class="o">=</span><span class="n">dilation</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="n">dilation</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">conv1x1</span><span class="p">(</span><span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;1x1 convolution&quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>


<span class="k">class</span> <span class="nc">BasicBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="n">expansion</span> <span class="o">=</span> <span class="mi">1</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">downsample</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                 <span class="n">base_width</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">norm_layer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">BasicBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="k">if</span> <span class="n">norm_layer</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">norm_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span>
        <span class="k">if</span> <span class="n">groups</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">base_width</span> <span class="o">!=</span> <span class="mi">64</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;BasicBlock only supports groups=1 and base_width=64&#39;</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">dilation</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Dilation &gt; 1 not supported in BasicBlock&quot;</span><span class="p">)</span>
        <span class="c1"># Both self.conv1 and self.downsample layers downsample the input when stride != 1</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">conv3x3</span><span class="p">(</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">stride</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span> <span class="o">=</span> <span class="n">norm_layer</span><span class="p">(</span><span class="n">planes</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">conv3x3</span><span class="p">(</span><span class="n">planes</span><span class="p">,</span> <span class="n">planes</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span> <span class="o">=</span> <span class="n">norm_layer</span><span class="p">(</span><span class="n">planes</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="n">downsample</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">=</span> <span class="n">stride</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">identity</span> <span class="o">=</span> <span class="n">x</span>

        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>

        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">identity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="n">out</span> <span class="o">+=</span> <span class="n">identity</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">out</span>


<span class="k">class</span> <span class="nc">Bottleneck</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="c1"># Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)</span>
    <span class="c1"># while original implementation places the stride at the first 1x1 convolution(self.conv1)</span>
    <span class="c1"># according to &quot;Deep residual learning for image recognition&quot;https://arxiv.org/abs/1512.03385.</span>
    <span class="c1"># This variant is also known as ResNet V1.5 and improves accuracy according to</span>
    <span class="c1"># https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.</span>

    <span class="n">expansion</span> <span class="o">=</span> <span class="mi">4</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">downsample</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                 <span class="n">base_width</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">norm_layer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Bottleneck</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="k">if</span> <span class="n">norm_layer</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">norm_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span>
        <span class="n">width</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">planes</span> <span class="o">*</span> <span class="p">(</span><span class="n">base_width</span> <span class="o">/</span> <span class="mf">64.</span><span class="p">))</span> <span class="o">*</span> <span class="n">groups</span>
        <span class="c1"># Both self.conv2 and self.downsample layers downsample the input when stride != 1</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">conv1x1</span><span class="p">(</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">width</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span> <span class="o">=</span> <span class="n">norm_layer</span><span class="p">(</span><span class="n">width</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">conv3x3</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">dilation</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span> <span class="o">=</span> <span class="n">norm_layer</span><span class="p">(</span><span class="n">width</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">conv1x1</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">planes</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">expansion</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bn3</span> <span class="o">=</span> <span class="n">norm_layer</span><span class="p">(</span><span class="n">planes</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">expansion</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="n">downsample</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">=</span> <span class="n">stride</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">identity</span> <span class="o">=</span> <span class="n">x</span>

        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>

        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>

        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn3</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">identity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="n">out</span> <span class="o">+=</span> <span class="n">identity</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">out</span>


<span class="k">class</span> <span class="nc">ResNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">zero_init_residual</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                 <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">width_per_group</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">replace_stride_with_dilation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                 <span class="n">norm_layer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">ResNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="k">if</span> <span class="n">norm_layer</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">norm_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_norm_layer</span> <span class="o">=</span> <span class="n">norm_layer</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span> <span class="o">=</span> <span class="mi">64</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dilation</span> <span class="o">=</span> <span class="mi">1</span>
        <span class="k">if</span> <span class="n">replace_stride_with_dilation</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="c1"># each element in the tuple indicates if we should replace</span>
            <span class="c1"># the 2x2 stride with a dilated convolution instead</span>
            <span class="n">replace_stride_with_dilation</span> <span class="o">=</span> <span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">False</span><span class="p">]</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">replace_stride_with_dilation</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;replace_stride_with_dilation should be None &quot;</span>
                             <span class="s2">&quot;or a 3-element tuple, got </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">replace_stride_with_dilation</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">groups</span> <span class="o">=</span> <span class="n">groups</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">base_width</span> <span class="o">=</span> <span class="n">width_per_group</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
                               <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span> <span class="o">=</span> <span class="n">norm_layer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">maxpool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">layer1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">layers</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">layer2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">layers</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                                       <span class="n">dilate</span><span class="o">=</span><span class="n">replace_stride_with_dilation</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">layer3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="n">layers</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                                       <span class="n">dilate</span><span class="o">=</span><span class="n">replace_stride_with_dilation</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">layer4</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="n">layers</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                                       <span class="n">dilate</span><span class="o">=</span><span class="n">replace_stride_with_dilation</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">avgpool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">512</span> <span class="o">*</span> <span class="n">block</span><span class="o">.</span><span class="n">expansion</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">):</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">kaiming_normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;fan_out&#39;</span><span class="p">,</span> <span class="n">nonlinearity</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">GroupNorm</span><span class="p">)):</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>

        <span class="c1"># Zero-initialize the last BN in each residual branch,</span>
        <span class="c1"># so that the residual branch starts with zeros, and each residual block behaves like an identity.</span>
        <span class="c1"># This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677</span>
        <span class="k">if</span> <span class="n">zero_init_residual</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">Bottleneck</span><span class="p">):</span>
                    <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bn3</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
                <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">BasicBlock</span><span class="p">):</span>
                    <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bn2</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_make_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">blocks</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dilate</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="n">norm_layer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_norm_layer</span>
        <span class="n">downsample</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="n">previous_dilation</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dilation</span>
        <span class="k">if</span> <span class="n">dilate</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">dilation</span> <span class="o">*=</span> <span class="n">stride</span>
            <span class="n">stride</span> <span class="o">=</span> <span class="mi">1</span>
        <span class="k">if</span> <span class="n">stride</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span> <span class="o">!=</span> <span class="n">planes</span> <span class="o">*</span> <span class="n">block</span><span class="o">.</span><span class="n">expansion</span><span class="p">:</span>
            <span class="n">downsample</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
                <span class="n">conv1x1</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span> <span class="o">*</span> <span class="n">block</span><span class="o">.</span><span class="n">expansion</span><span class="p">,</span> <span class="n">stride</span><span class="p">),</span>
                <span class="n">norm_layer</span><span class="p">(</span><span class="n">planes</span> <span class="o">*</span> <span class="n">block</span><span class="o">.</span><span class="n">expansion</span><span class="p">),</span>
            <span class="p">)</span>

        <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">downsample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">groups</span><span class="p">,</span>
                            <span class="bp">self</span><span class="o">.</span><span class="n">base_width</span><span class="p">,</span> <span class="n">previous_dilation</span><span class="p">,</span> <span class="n">norm_layer</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span> <span class="o">=</span> <span class="n">planes</span> <span class="o">*</span> <span class="n">block</span><span class="o">.</span><span class="n">expansion</span>
        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">blocks</span><span class="p">):</span>
            <span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">groups</span><span class="p">,</span>
                                <span class="n">base_width</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">base_width</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dilation</span><span class="p">,</span>
                                <span class="n">norm_layer</span><span class="o">=</span><span class="n">norm_layer</span><span class="p">))</span>

        <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">layers</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_forward_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="c1"># See note [TorchScript super()]</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">maxpool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">layer1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">layer2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">layer3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">layer4</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">avgpool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">x</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_impl</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">_resnet</span><span class="p">(</span><span class="n">arch</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">ResNet</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">pretrained</span><span class="p">:</span>
        <span class="n">state_dict</span> <span class="o">=</span> <span class="n">load_state_dict_from_url</span><span class="p">(</span><span class="n">model_urls</span><span class="p">[</span><span class="n">arch</span><span class="p">],</span>
                                              <span class="n">progress</span><span class="o">=</span><span class="n">progress</span><span class="p">)</span>
        <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span>


<div class="viewcode-block" id="resnet18"><a class="viewcode-back" href="../../../models.html#torchvision.models.resnet18">[docs]</a><span class="k">def</span> <span class="nf">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;ResNet-18 model from</span>
<span class="sd">    `&quot;Deep Residual Learning for Image Recognition&quot; &lt;https://arxiv.org/pdf/1512.03385.pdf&gt;`_</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_resnet</span><span class="p">(</span><span class="s1">&#39;resnet18&#39;</span><span class="p">,</span> <span class="n">BasicBlock</span><span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span>
                   <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="resnet34"><a class="viewcode-back" href="../../../models.html#torchvision.models.resnet34">[docs]</a><span class="k">def</span> <span class="nf">resnet34</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;ResNet-34 model from</span>
<span class="sd">    `&quot;Deep Residual Learning for Image Recognition&quot; &lt;https://arxiv.org/pdf/1512.03385.pdf&gt;`_</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_resnet</span><span class="p">(</span><span class="s1">&#39;resnet34&#39;</span><span class="p">,</span> <span class="n">BasicBlock</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span>
                   <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="resnet50"><a class="viewcode-back" href="../../../models.html#torchvision.models.resnet50">[docs]</a><span class="k">def</span> <span class="nf">resnet50</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;ResNet-50 model from</span>
<span class="sd">    `&quot;Deep Residual Learning for Image Recognition&quot; &lt;https://arxiv.org/pdf/1512.03385.pdf&gt;`_</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_resnet</span><span class="p">(</span><span class="s1">&#39;resnet50&#39;</span><span class="p">,</span> <span class="n">Bottleneck</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span>
                   <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="resnet101"><a class="viewcode-back" href="../../../models.html#torchvision.models.resnet101">[docs]</a><span class="k">def</span> <span class="nf">resnet101</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;ResNet-101 model from</span>
<span class="sd">    `&quot;Deep Residual Learning for Image Recognition&quot; &lt;https://arxiv.org/pdf/1512.03385.pdf&gt;`_</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_resnet</span><span class="p">(</span><span class="s1">&#39;resnet101&#39;</span><span class="p">,</span> <span class="n">Bottleneck</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">23</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span>
                   <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="resnet152"><a class="viewcode-back" href="../../../models.html#torchvision.models.resnet152">[docs]</a><span class="k">def</span> <span class="nf">resnet152</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;ResNet-152 model from</span>
<span class="sd">    `&quot;Deep Residual Learning for Image Recognition&quot; &lt;https://arxiv.org/pdf/1512.03385.pdf&gt;`_</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_resnet</span><span class="p">(</span><span class="s1">&#39;resnet152&#39;</span><span class="p">,</span> <span class="n">Bottleneck</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">36</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span>
                   <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="resnext50_32x4d"><a class="viewcode-back" href="../../../models.html#torchvision.models.resnext50_32x4d">[docs]</a><span class="k">def</span> <span class="nf">resnext50_32x4d</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;ResNeXt-50 32x4d model from</span>
<span class="sd">    `&quot;Aggregated Residual Transformation for Deep Neural Networks&quot; &lt;https://arxiv.org/pdf/1611.05431.pdf&gt;`_</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;groups&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">32</span>
    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;width_per_group&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">4</span>
    <span class="k">return</span> <span class="n">_resnet</span><span class="p">(</span><span class="s1">&#39;resnext50_32x4d&#39;</span><span class="p">,</span> <span class="n">Bottleneck</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
                   <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="resnext101_32x8d"><a class="viewcode-back" href="../../../models.html#torchvision.models.resnext101_32x8d">[docs]</a><span class="k">def</span> <span class="nf">resnext101_32x8d</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;ResNeXt-101 32x8d model from</span>
<span class="sd">    `&quot;Aggregated Residual Transformation for Deep Neural Networks&quot; &lt;https://arxiv.org/pdf/1611.05431.pdf&gt;`_</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;groups&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">32</span>
    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;width_per_group&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">8</span>
    <span class="k">return</span> <span class="n">_resnet</span><span class="p">(</span><span class="s1">&#39;resnext101_32x8d&#39;</span><span class="p">,</span> <span class="n">Bottleneck</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">23</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
                   <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="wide_resnet50_2"><a class="viewcode-back" href="../../../models.html#torchvision.models.wide_resnet50_2">[docs]</a><span class="k">def</span> <span class="nf">wide_resnet50_2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Wide ResNet-50-2 model from</span>
<span class="sd">    `&quot;Wide Residual Networks&quot; &lt;https://arxiv.org/pdf/1605.07146.pdf&gt;`_</span>

<span class="sd">    The model is the same as ResNet except for the bottleneck number of channels</span>
<span class="sd">    which is twice larger in every block. The number of channels in outer 1x1</span>
<span class="sd">    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048</span>
<span class="sd">    channels, and in Wide ResNet-50-2 has 2048-1024-2048.</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;width_per_group&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">64</span> <span class="o">*</span> <span class="mi">2</span>
    <span class="k">return</span> <span class="n">_resnet</span><span class="p">(</span><span class="s1">&#39;wide_resnet50_2&#39;</span><span class="p">,</span> <span class="n">Bottleneck</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
                   <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="wide_resnet101_2"><a class="viewcode-back" href="../../../models.html#torchvision.models.wide_resnet101_2">[docs]</a><span class="k">def</span> <span class="nf">wide_resnet101_2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Wide ResNet-101-2 model from</span>
<span class="sd">    `&quot;Wide Residual Networks&quot; &lt;https://arxiv.org/pdf/1605.07146.pdf&gt;`_</span>

<span class="sd">    The model is the same as ResNet except for the bottleneck number of channels</span>
<span class="sd">    which is twice larger in every block. The number of channels in outer 1x1</span>
<span class="sd">    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048</span>
<span class="sd">    channels, and in Wide ResNet-50-2 has 2048-1024-2048.</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;width_per_group&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">64</span> <span class="o">*</span> <span class="mi">2</span>
    <span class="k">return</span> <span class="n">_resnet</span><span class="p">(</span><span class="s1">&#39;wide_resnet101_2&#39;</span><span class="p">,</span> <span class="n">Bottleneck</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">23</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
                   <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
</pre></div>

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